suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(gapminder))
library(gapminder)
library(tidyverse)
library(gganimate)

Task #1 Get the maximum and minimum of GDP per capita for all continents.

gdpPercap_continent <- gapminder %>% 
  group_by(continent) %>% 
  summarise(minGDP = min(gdpPercap), maxGDP = max(gdpPercap)) 

knitr::kable(gdpPercap_continent, col.names = c("Continnet", "min gdpPercap", "max gdpPercap"))
Continnet min gdpPercap max gdpPercap
Africa 241.1659 21951.21
Americas 1201.6372 42951.65
Asia 331.0000 113523.13
Europe 973.5332 49357.19
Oceania 10039.5956 34435.37

Then, plot the figures.

The minimum gdpPercap for each continent:
gdpPercap_continent %>% 
  ggplot(aes(continent, minGDP, fill=continent)) + 
  geom_bar(stat="identity") +
  ggtitle("min gdpPercap for each continent")

The maximum gdpPercap for each continent:
gdpPercap_continent %>% 
  ggplot(aes(continent, maxGDP, fill=continent)) + 
  geom_bar(stat="identity") +
  ggtitle("max gdpPercap for each continent")

Task #2 Look at the spread of GDP per capita within the continents.

Generate the spread of GDP per capita for each continent by calculating the minimum, mean, median, and maximum of gdpPercap

gapminder %>% 
  group_by(continent) %>% 
  summarise(min = min(gdpPercap), mean = mean(gdpPercap), 
            median = median(gdpPercap), max = max(gdpPercap)) %>% 
  knitr::kable()
continent min mean median max
Africa 241.1659 2193.755 1192.138 21951.21
Americas 1201.6372 7136.110 5465.510 42951.65
Asia 331.0000 7902.150 2646.787 113523.13
Europe 973.5332 14469.476 12081.749 49357.19
Oceania 10039.5956 18621.609 17983.304 34435.37 
gapminder %>% 
ggplot(aes(continent, log(gdpPercap), fill=continent)) + 
  geom_boxplot()

Task #3 Compute a trimmed mean of life expectancy for different years. Or a weighted mean, weighting by population. Just try something other than the plain vanilla mean.

I calcualte the weighted mean of life expectancy by pop for each continent and plot the change over time as followng:

gapminder %>% 
  select(continent, year, lifeExp, pop) %>% 
  group_by(continent, year) %>% 
  summarise(mean = weighted.mean(lifeExp, pop)) 
## # A tibble: 60 x 3
## # Groups:   continent [?]
##    continent  year  mean
##    <fct>     <int> <dbl>
##  1 Africa     1952  38.8
##  2 Africa     1957  40.9
##  3 Africa     1962  43.1
##  4 Africa     1967  45.2
##  5 Africa     1972  47.2
##  6 Africa     1977  49.2
##  7 Africa     1982  51.0
##  8 Africa     1987  52.8
##  9 Africa     1992  53.4
## 10 Africa     1997  53.3
## # ... with 50 more rows
gapminder %>%
  group_by(continent, year) %>% 
  summarise(mean = weighted.mean(lifeExp, pop)) %>% 
  ggplot(aes(year, mean)) +
  geom_smooth(method="lm", se=TRUE) + # linear regression 
  geom_point(aes(colour = continent)) +
  ggtitle("Weighted Mean life expectancy changes over time for each continent")

Task #4 How is life expectancy changing over time on different continents?

I calcualte the mean of life expectancy for each continent and plot the change over time as followng:

gapminder %>% 
  select(continent, year, lifeExp, pop) %>% 
  group_by(continent, year) %>% 
  summarise(mean = mean(lifeExp)) 
## # A tibble: 60 x 3
## # Groups:   continent [?]
##    continent  year  mean
##    <fct>     <int> <dbl>
##  1 Africa     1952  39.1
##  2 Africa     1957  41.3
##  3 Africa     1962  43.3
##  4 Africa     1967  45.3
##  5 Africa     1972  47.5
##  6 Africa     1977  49.6
##  7 Africa     1982  51.6
##  8 Africa     1987  53.3
##  9 Africa     1992  53.6
## 10 Africa     1997  53.6
## # ... with 50 more rows
gapminder %>%
  group_by(continent, year) %>% 
  summarise(mean = mean(lifeExp)) %>% 
  ggplot(aes(year, mean)) +
  geom_smooth(method="lm", se=TRUE) + # linear regression 
  geom_point(aes(colour = continent)) +
  ggtitle("Mean life expectancy changes over time for each continent")

To get better visualization of how the life expectancy changes over time on different continents, I used the gganimate library to generate dynamic figures (gif).

theme_set(theme_bw())  # pre-set the bw theme.
gapminder %>% 
  ggplot(aes(log(gdpPercap), lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  facet_wrap(~continent, scales = "free") +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

But I want to do more!

Layout stretch goal: get table and figure side-by-side. This gist might get you started.

library(ggplot2)
suppressMessages(library(dplyr))
#devtools::install_github("jennybc/gapminder")
library("gapminder")
gtbl <- tbl_df(gapminder)
tbl1 <- gtbl %>%
  group_by(continent) %>% 
  summarise_each(funs(mean, median), gdpPercap) 
## `summarise_each()` is deprecated.
## Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
## To map `funs` over a selection of variables, use `summarise_at()`
knitr::kable(tbl1)
continent mean median
Africa 2193.755 1192.138
Americas 7136.110 5465.510
Asia 7902.150 2646.787
Europe 14469.476 12081.749
Oceania 18621.609 17983.304 
ggplot(gtbl, aes(x = continent, y = gdpPercap, color = continent)) +
  geom_point() + ggtitle("GDP per cap by continent")

It’s not working perfect.